library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
  method         from
  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ ggplot2 3.3.5     ✓ purrr   0.3.4
✓ tibble  3.1.3     ✓ dplyr   1.0.7
✓ tidyr   1.1.3     ✓ stringr 1.4.0
✓ readr   2.0.1     ✓ forcats 0.5.1
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
library(janitor)

Attaching package: ‘janitor’

The following objects are masked from ‘package:stats’:

    chisq.test, fisher.test
library(readODS)
library(httr)
library(here)
here() starts at /Users/tomdavie/Documents/GitHub/ev_climate_change_project
library(data.table)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
data.table 1.14.0 using 1 threads (see ?getDTthreads).  Latest news: r-datatable.com
**********
This installation of data.table has not detected OpenMP support. It should still work but in single-threaded mode.
This is a Mac. Please read https://mac.r-project.org/openmp/. Please engage with Apple and ask them for support. Check r-datatable.com for updates, and our Mac instructions here: https://github.com/Rdatatable/data.table/wiki/Installation. After several years of many reports of installation problems on Mac, it's time to gingerly point out that there have been no similar problems on Windows or Linux.
**********

Attaching package: ‘data.table’

The following objects are masked from ‘package:dplyr’:

    between, first, last

The following object is masked from ‘package:purrr’:

    transpose
library(sf)
Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1
library(leaflet)
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
read_ods(here("raw_data/electric-vehicle-charging-device-statistics-june-2021.ods"))
Error in .parse_ods_file(file) : file does not exist
getwd()
APE <- read_ods("raw_data/APE_site_data_tables.ods", sheet = 2)
NO2 <- read_ods("raw_data/NO2_tables.ods")
registry <- GET("http://chargepoints.dft.gov.uk/api/retrieve/registry/format/csv/") %>% 
  content()
type <- GET("http://chargepoints.dft.gov.uk/api/retrieve/type/format/csv/") %>% 
  content()
read_csv("Documents/GitHub/ev_climate_change_project/raw_data/no2_by_grid_2019.csv")
air_pollution_cap <- GET("https://uk-air.defra.gov.uk/data/sos/service?service=AQD&request=GetCapabilities")
air_pollution_cap <- rbindlist(air_pollution$content, fill = TRUE)
air_pollution_cap <- unnest(air_pollution_cap)
air_pollution <- GET("https://uk-air.defra.gov.uk/data/sos/service?service=SOS&version=2.0.0&request=GetObserved&observedProperty=http://dd.eionet.europa.eu/vocabulary/aq/pollutant/8") %>% 
  content()
air_pollution$exceptions
air_pollution <- rbindlist(air_pollution, fill = TRUE) 
Error in rbindlist(air_pollution, fill = TRUE) : 
  Input is data.table but should be a plain list of items to be stacked
air_pollution <- unnest(air_pollution)

How many electric vehicles are on the road across the UK by LA?

# Reading in and skipping first 5 rows
uk_ev <- read_ods(here("raw_data/ev_by_la.ods"), sheet = 2, skip = 5)
# Making row 1 the variable name
names(uk_ev) <- uk_ev[1,] 
# Removing row 1 
uk_ev <- uk_ev[-1,] %>% 
  clean_names()
# loading in shape file 
uk_shape_file <- st_read(here("raw_data/Local_Authority_Districts__April_2019__UK_BFE_v2-shp/Local_Authority_Districts__April_2019__UK_BFE_v2.shp")) %>%
  clean_names() %>% 
  st_simplify(dTolerance = 1000) %>%
  st_transform("+proj=longlat +datum=WGS84") %>% 
  select(lad19cd, long, lat, geometry) 
Error in (function (cond)  : 
  error in evaluating the argument 'x' in selecting a method for function 'select': Cannot open "/Users/tomdavie/Documents/GitHub/ev_climate_change_project/raw_data/Local_Authority_Districts__April_2019__UK_BFE_v2-shp/Local_Authority_Districts__April_2019__UK_BFE_v2.shp"; The file doesn't seem to exist.
# Joining uk_ev + shape file 
uk_ev_map <- uk_ev %>% 
  left_join(uk_shape_file, by = c("ons_la_code_apr_2019" = "lad19cd")) %>% 
  drop_na() %>% 
  mutate(across(c(x2021_q1:x2011_q4), as.numeric)) %>% 
  st_as_sf()
    pal <- colorBin("Greens", domain = uk_ev_map$x2021_q1, bins = c(0, 500, 1000, 2500, 5000, 10000, 15000))
    
    uk_ev_map_labels <- sprintf(
      "<strong>%s</strong><br/>%g Electric Vehicles",
      uk_ev_map$region_local_authority_apr_2019_3, uk_ev_map$x2021_q1) %>% 
      lapply(htmltools::HTML)
# Geospatial of EV Vehicles in the UK 2021 Q1
uk_ev_map %>% 
  leaflet() %>% 
  setView(lng = -4.2026, lat = 55.8, zoom = 4.7, options = list()) %>%
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addPolygons(fillColor = ~pal(x2021_q1),
    weight = 0.1,
    opacity = 0.9, 
    color = "black",
    fillOpacity = 0.8,
    highlightOptions = highlightOptions(color = "green", weight = 2,
                                        bringToFront = TRUE),
    label = uk_ev_map_labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>% 
  addLegend(pal = pal, values = ~x2021_q1, opacity = 0.7, title = NULL,
            position = "bottomright")

How many electric vehicles are on the road across the UK by LA?

# Reading in and skipping first 5 rows
uk_ev_postcode <- read_ods(here("raw_data/ev_by_postcode.ods"), sheet = 2, skip = 6) %>% 
  rename("postcode" = PostcodeDistrict2)
Error: Can't rename columns that don't exist.
x Column `PostcodeDistrict2` doesn't exist.
Run `rlang::last_error()` to see where the error occurred.
# loading in shape file 
uk_shape_file_postcode <- st_read(here("raw_data/postcode_shape/EX_Sample.shp")) %>%
  clean_names() %>% 
  st_simplify(dTolerance = 1000) %>%
  st_transform("+proj=longlat +datum=WGS84") #%>% 
#  select(lad19cd, long, lat, geometry) 
# Joining uk_ev + shape file 
uk_ev_map_postcode <- uk_ev_postcode %>% 
  left_join(uk_shape_file_postcode, by = c("ons_la_code_apr_2019" = "lad19cd")) %>% 
  drop_na() %>% 
  mutate(across(c(x2021_q1:x2011_q4), as.numeric)) %>% 
  st_as_sf()
    pal <- colorBin("Greens", domain = uk_ev_map$x2021_q1, bins = c(0, 500, 1000, 2500, 5000, 10000, 15000))
    
    uk_ev_map_labels <- sprintf(
      "<strong>%s</strong><br/>%g Electric Vehicles",
      uk_ev_map$region_local_authority_apr_2019_3, uk_ev_map$x2021_q1) %>% 
      lapply(htmltools::HTML)
# Geospatial of EV Vehicles in the UK 2021 Q1
uk_ev_map %>% 
  leaflet() %>% 
  setView(lng = -4.2026, lat = 55.8, zoom = 4.7, options = list()) %>%
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addPolygons(fillColor = ~pal(x2021_q1),
    weight = 0.1,
    opacity = 0.9, 
    color = "black",
    fillOpacity = 0.8,
    highlightOptions = highlightOptions(color = "green", weight = 2,
                                        bringToFront = TRUE),
    label = uk_ev_map_labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>% 
  addLegend(pal = pal, values = ~x2021_q1, opacity = 0.7, title = NULL,
            position = "bottomright")
# Wrangling to create an EV count over time plot 
uk_ev_longer <- uk_ev %>%
  # Pivot longer to get year and count columns
  pivot_longer(cols = c(x2021_q1:x2011_q4), names_to = c("year"), values_to = "no_of_ev") %>% 
  # Filter so we only have UK as a whole data AND we only want final numbers of the year so Q4 
  filter(region_local_authority_apr_2019_3 == "United Kingdom" & str_detect(year, "q4")) %>% 
  # Simplify to just show year
  mutate(year = case_when(str_detect(year, "2021") ~ "2021",
         str_detect(year, "2020") ~ "2020",
         str_detect(year, "2019") ~ "2019",
         str_detect(year, "2018") ~ "2018",
         str_detect(year, "2017") ~ "2017",
         str_detect(year, "2016") ~ "2016",
         str_detect(year, "2015") ~ "2015",
         str_detect(year, "2014") ~ "2014",
         str_detect(year, "2013") ~ "2013",
         str_detect(year, "2012") ~ "2012",
         str_detect(year, "2011") ~ "2011"),
         year = as.numeric(year),
         no_of_ev = as.numeric(no_of_ev))

Row binding grid NO2 data

no2_2010 <- read_csv(here("raw_data/no2_by_grid_2010.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2011 <- read_csv(here("raw_data/no2_by_grid_2011.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2012 <- read_csv(here("raw_data/no2_by_grid_2012.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2013 <- read_csv(here("raw_data/no2_by_grid_2013.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2014 <- read_csv(here("raw_data/no2_by_grid_2014.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2015 <- read_csv(here("raw_data/no2_by_grid_2015.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2016 <- read_csv(here("raw_data/no2_by_grid_2016.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2017 <- read_csv(here("raw_data/no2_by_grid_2017.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2018 <- read_csv(here("raw_data/no2_by_grid_2018.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2019 <- read_csv(here("raw_data/no2_by_grid_2019.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))

# Create empty data frame
no2_all <- data_frame()

# For each year, bind rows to one dataset
for (i in 2010:2019) {
  df_name <- paste0("no2_", i)
  df_input <- as.name(df_name)
  
  df <- eval(df_input) %>% 
    mutate(year = i)
  
no2_all <- bind_rows(no2_all, df)
  }
# Remove missing NO2 grid values
no2_clean <- no2_all %>% 
  filter(no2 != "MISSING")
library(proj4)
proj4string <- "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +datum=OSGB36 +units=m +no_defs"

no2_clean_row <- no2_clean %>% 
  rowid_to_column()

# Source data
xy <- no2_clean_row %>% 
  select(x, y, rowid)

# Transformed data
pj <- project(xy, proj4string, inverse=TRUE)
latlon <- data.frame(xy, lat=pj$y, lon=pj$x)
final <-  merge(no2_clean_row, latlon, by.x = "rowid", by.y = "rowid") %>%
  filter(year == 2019) %>% 
  select(lat, lon, no2) 
final %>% 
  head()
no2_annual_mean <- read_ods(here("raw_data/NO2_tables.ods"), sheet = 3, skip = 2) %>% 
  clean_names()
no2_annual_mean_all %>% 
  ggplot() +
  aes(x = year, y = annual_mean_no2concentration_mg_m3) + 
  geom_point() +
  geom_smooth(se = FALSE) +
  theme_minimal() +
  scale_y_continuous(breaks = seq(0, 70, 10), limits = c(0, 70)) +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  labs(x = "\nYear\n", 
       y = "\nAnnual Mean NO2 Conc mg m3\n",
       title = "\nNO2 over time in the UK\n") 
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

forecast_no2 <- no2_annual_mean_all %>% 
  dplyr::select(year, annual_mean_no2concentration_mg_m3) %>% 
  tsibble()
Error: Can't determine index and please specify argument `index`.
Run `rlang::last_error()` to see where the error occurred.
autoplot(forecast_no2)

---
title: "R Notebook"
output: html_notebook
---

```{r}
library(tidyverse)
library(janitor)
library(readODS)
library(httr)
library(here)
library(data.table)
library(sf)
library(leaflet)
library(leaflet.extras)
```

```{r}
read_ods(here("raw_data/electric-vehicle-charging-device-statistics-june-2021.ods"))
```

```{r}
getwd()
```

```{r}
APE <- read_ods("raw_data/APE_site_data_tables.ods", sheet = 2)
```

```{r}
NO2 <- read_ods("raw_data/NO2_tables.ods")
```

```{r}
registry <- GET("http://chargepoints.dft.gov.uk/api/retrieve/registry/format/csv/") %>% 
  content()
```

```{r}
type <- GET("http://chargepoints.dft.gov.uk/api/retrieve/type/format/csv/") %>% 
  content()
```

```{r}
read_csv("Documents/GitHub/ev_climate_change_project/raw_data/no2_by_grid_2019.csv")
```


```{r}
air_pollution_cap <- GET("https://uk-air.defra.gov.uk/data/sos/service?service=AQD&request=GetCapabilities")
```

```{r}
air_pollution_cap <- rbindlist(air_pollution$content, fill = TRUE)
```

```{r}
air_pollution_cap <- unnest(air_pollution_cap)
```


```{r}
air_pollution <- GET("https://uk-air.defra.gov.uk/data/sos/service?service=SOS&version=2.0.0&request=GetObserved&observedProperty=http://dd.eionet.europa.eu/vocabulary/aq/pollutant/8") %>% 
  content()
```

```{r}
air_pollution$exceptions
```

```{r}
air_pollution <- rbindlist(air_pollution$exceptions, fill = TRUE) 
```

```{r}
air_pollution <- unnest(air_pollution)
```   

# How many electric vehicles are on the road across the UK by LA?

```{r}
# Reading in and skipping first 5 rows
uk_ev <- read_ods(here("raw_data/ev_by_la.ods"), sheet = 2, skip = 5)
# Making row 1 the variable name
names(uk_ev) <- uk_ev[1,] 
# Removing row 1 
uk_ev <- uk_ev[-1,] %>% 
  clean_names()
```

```{r}
# loading in shape file 
uk_shape_file <- st_read(here("raw_data/Local_Authority_Districts__April_2019__UK_BFE_v2-shp/Local_Authority_Districts__April_2019__UK_BFE_v2.shp")) %>%
  clean_names() %>% 
  st_simplify(dTolerance = 1000) %>%
  st_transform("+proj=longlat +datum=WGS84") %>% 
  select(lad19cd, long, lat, geometry) 
```

```{r}
# Joining uk_ev + shape file 
uk_ev_map <- uk_ev %>% 
  left_join(uk_shape_file, by = c("ons_la_code_apr_2019" = "lad19cd")) %>% 
  drop_na() %>% 
  mutate(across(c(x2021_q1:x2011_q4), as.numeric)) %>% 
  st_as_sf()
```


```{r}
    pal <- colorBin("Greens", domain = uk_ev_map$x2021_q1, bins = c(0, 500, 1000, 2500, 5000, 10000, 15000))
    
    uk_ev_map_labels <- sprintf(
      "<strong>%s</strong><br/>%g Electric Vehicles",
      uk_ev_map$region_local_authority_apr_2019_3, uk_ev_map$x2021_q1) %>% 
      lapply(htmltools::HTML)
```

```{r}
# Geospatial of EV Vehicles in the UK 2021 Q1
uk_ev_map %>% 
  leaflet() %>% 
  setView(lng = -4.2026, lat = 55.8, zoom = 4.7, options = list()) %>%
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addPolygons(fillColor = ~pal(x2021_q1),
    weight = 0.1,
    opacity = 0.9, 
    color = "black",
    fillOpacity = 0.8,
    highlightOptions = highlightOptions(color = "green", weight = 2,
                                        bringToFront = TRUE),
    label = uk_ev_map_labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>% 
  addLegend(pal = pal, values = ~x2021_q1, opacity = 0.7, title = NULL,
            position = "bottomright")
```
# How many electric vehicles are on the road across the UK by LA?

```{r}
# Reading in and skipping first 5 rows
uk_ev_postcode <- read_ods(here("raw_data/ev_by_postcode.ods"), sheet = 2, skip = 6) %>% 
  rename("postcode" = "Postcode District2")
```

```{r}
# loading in shape file 
uk_shape_file_postcode <- st_read(here("raw_data/postcode_shape/EX_Sample.shp")) %>%
  clean_names() %>% 
  st_simplify(dTolerance = 1000) %>%
  st_transform("+proj=longlat +datum=WGS84") #%>% 
#  select(lad19cd, long, lat, geometry) 
```

```{r}
# Joining uk_ev + shape file 
uk_ev_map_postcode <- uk_ev_postcode %>% 
  left_join(uk_shape_file_postcode, by = c("ons_la_code_apr_2019" = "lad19cd")) %>% 
  drop_na() %>% 
  mutate(across(c(x2021_q1:x2011_q4), as.numeric)) %>% 
  st_as_sf()
```


```{r}
    pal <- colorBin("Greens", domain = uk_ev_map$x2021_q1, bins = c(0, 500, 1000, 2500, 5000, 10000, 15000))
    
    uk_ev_map_labels <- sprintf(
      "<strong>%s</strong><br/>%g Electric Vehicles",
      uk_ev_map$region_local_authority_apr_2019_3, uk_ev_map$x2021_q1) %>% 
      lapply(htmltools::HTML)
```

```{r}
# Geospatial of EV Vehicles in the UK 2021 Q1
uk_ev_map %>% 
  leaflet() %>% 
  setView(lng = -4.2026, lat = 55.8, zoom = 4.7, options = list()) %>%
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addPolygons(fillColor = ~pal(x2021_q1),
    weight = 0.1,
    opacity = 0.9, 
    color = "black",
    fillOpacity = 0.8,
    highlightOptions = highlightOptions(color = "green", weight = 2,
                                        bringToFront = TRUE),
    label = uk_ev_map_labels,
    labelOptions = labelOptions(
      style = list("font-weight" = "normal", padding = "3px 8px"),
      textsize = "15px",
      direction = "auto")) %>% 
  addLegend(pal = pal, values = ~x2021_q1, opacity = 0.7, title = NULL,
            position = "bottomright")
```
  
```{r}
# Wrangling to create an EV count over time plot 
uk_ev_longer <- uk_ev %>%
  # Pivot longer to get year and count columns
  pivot_longer(cols = c(x2021_q1:x2011_q4), names_to = c("year"), values_to = "no_of_ev") %>% 
  # Filter so we only have UK as a whole data AND we only want final numbers of the year so Q4 
  filter(region_local_authority_apr_2019_3 == "United Kingdom" & str_detect(year, "q4")) %>% 
  # Simplify to just show year
  mutate(year = case_when(str_detect(year, "2021") ~ "2021",
         str_detect(year, "2020") ~ "2020",
         str_detect(year, "2019") ~ "2019",
         str_detect(year, "2018") ~ "2018",
         str_detect(year, "2017") ~ "2017",
         str_detect(year, "2016") ~ "2016",
         str_detect(year, "2015") ~ "2015",
         str_detect(year, "2014") ~ "2014",
         str_detect(year, "2013") ~ "2013",
         str_detect(year, "2012") ~ "2012",
         str_detect(year, "2011") ~ "2011"),
         year = as.numeric(year),
         no_of_ev = as.numeric(no_of_ev))
```


```{r}
uk_ev_longer %>% 
  ggplot() +
  aes(x = year, y = no_of_ev) +
  geom_col(fill = "lawngreen") +
  scale_x_continuous(breaks = c(2011:2020)) +
  scale_y_continuous(breaks = seq(0, 220000, by = 20000), limits = c(0, 220000)) +
  labs(title = "\nNumber of Electric Vehicles over time in the UK\n",
       x = "\nYear\n",
       y = "\nNumber of Electric Vehicles\n") +
  theme_minimal() 
```

  


# Row binding grid NO2 data 

```{r}
no2_2010 <- read_csv(here("raw_data/no2_by_grid_2010.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2011 <- read_csv(here("raw_data/no2_by_grid_2011.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2012 <- read_csv(here("raw_data/no2_by_grid_2012.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2013 <- read_csv(here("raw_data/no2_by_grid_2013.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2014 <- read_csv(here("raw_data/no2_by_grid_2014.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2015 <- read_csv(here("raw_data/no2_by_grid_2015.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2016 <- read_csv(here("raw_data/no2_by_grid_2016.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2017 <- read_csv(here("raw_data/no2_by_grid_2017.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2018 <- read_csv(here("raw_data/no2_by_grid_2018.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))
no2_2019 <- read_csv(here("raw_data/no2_by_grid_2019.csv"), skip = 6, 
  col_names = c("uk_grid_code", "x", "y", "no2"))

# Create empty data frame
no2_all <- data_frame()

# For each year, bind rows to one dataset
for (i in 2010:2019) {
  df_name <- paste0("no2_", i)
  df_input <- as.name(df_name)
  
  df <- eval(df_input) %>% 
    mutate(year = i)
  
no2_all <- bind_rows(no2_all, df)
  }
```

```{r}
# Remove missing NO2 grid values
no2_clean <- no2_all %>% 
  filter(no2 != "MISSING")
```

```{r}
library(proj4)
proj4string <- "+proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +datum=OSGB36 +units=m +no_defs"

no2_clean_row <- no2_clean %>% 
  rowid_to_column()

# Source data
xy <- no2_clean_row %>% 
  select(x, y, rowid)

# Transformed data
pj <- project(xy, proj4string, inverse=TRUE)
latlon <- data.frame(xy, lat=pj$y, lon=pj$x)
final <-  merge(no2_clean_row, latlon, by.x = "rowid", by.y = "rowid") %>%
  filter(year == 2019) %>% 
  select(lat, lon, no2) 
```

```{r}
final <- final %>% 
  mutate(no2 = as.numeric(no2)) 
```

```{r}
bins <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
pal <- colorBin("Spectral", domain = final$no2, bins = bins, na.color = "transparent", reverse = TRUE)

leaflet() %>%
  addProviderTiles("CartoDB.Positron", options = providerTileOptions(noWrap = TRUE)) %>%
  setView(lng = -4.2026, lat = 55.8, zoom = 4.7, options = list()) %>%
  addHeatmap(data = final,
             lng = ~lon,
             lat = ~lat,
             intensity = ~no2,
             minOpacity = 0.1,
             max = 40,
             radius = 1,
             blur = 1) %>% 
  addLegend(pal = pal, values = final$no2,
                title="Average NO2 Conc")
```

```{r}
final %>% 
  head()
```

```{r}
write_csv(final, "no2_lat_long_data.csv")
```

```{r}
no2_annual_mean <- read_ods(here("raw_data/NO2_tables.ods"), sheet = 3, skip = 2) %>% 
  clean_names()
```

```{r}
no2_annual_mean_all <- no2_annual_mean %>% 
  filter(site == "All sites") %>% 
  mutate(year = as.numeric(year))
```

```{r}
no2_annual_mean_all %>% 
  ggplot() +
  aes(x = year, y = annual_mean_no2concentration_mg_m3) + 
  geom_point() +
  geom_smooth(se = FALSE) +
  theme_minimal() +
  scale_y_continuous(breaks = seq(0, 70, 10), limits = c(0, 70)) +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.5, hjust=1)) +
  labs(x = "\nYear\n", 
       y = "\nAnnual Mean NO2 Conc mg m3\n",
       title = "\nNO2 over time in the UK\n") 
```


```{r}
library(tidyverse)
library(fable)
library(tsibble)
library(tsibbledata)
library(ggfortify)
```

```{r}
forecast_no2 <- no2_annual_mean_all %>% 
  dplyr::select(year, annual_mean_no2concentration_mg_m3) %>% 
  tsibble(index = year)
```

```{r}
autoplot(forecast_no2)
```

```{r}
fit <- forecast_no2 %>%
  model(
    mean_model = MEAN(annual_mean_no2concentration_mg_m3),
    arima = ARIMA(annual_mean_no2concentration_mg_m3)
  )
fit
```

```{r}
forecast_1 <- fit %>%
  fabletools::forecast(h = 15)
forecast_1
```

```{r}
forecast_1 %>%
  autoplot(forecast_no2) +
  ggtitle("Forecasts for Australian beer sales") +
  xlab("Year") +
  guides(colour = guide_legend(title = "Forecast"))
```

